ICNC 2005: Advances in Natural Computation pp 1072-1079 | Cite as

A Novel Clustering Fitness Sharing Genetic Algorithm

  • Xinjie Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)

Abstract

The hybrid multimodal optimization algorithm that combines a novel clustering method and fitness sharing method is presented in this paper. The only parameter required by the novel clustering method is the peak number. The clustering criteria include minimizing the square sum of the inner-group distance, maximizing the square sum of the inter-group distance, and the fitness value of the individuals. After each individual has been classified to the certain cluster, fitness sharing genetic algorithm is used to find multiple peaks simultaneously. The empirical study of the benchmark problems shows that the proposed method has satisfactory performance.

Keywords

Genetic Algorithm Cluster Method Benchmark Problem Peak Center Cluster Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)MATHGoogle Scholar
  3. 3.
    Mahfoud, S.W.: Genetic Drift in Sharing Methods. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 67–72. IEEE Press, Piscataway (1994)CrossRefGoogle Scholar
  4. 4.
    Goldberg, D.E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pp. 41–49. Lawrence Erlbaum, Hillsdale (1987)Google Scholar
  5. 5.
    Petrowski, A.: A Clearing Procedure as a Niching Method for Genetic Algorithms. In: Proceedings of the third IEEE Conference on Evolutionary Computation, pp. 798–803. IEEE Press, Piscataway (1996)CrossRefGoogle Scholar
  6. 6.
    Miller, B.L., Shaw, M.J.: Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization. In: Proceedings of the third IEEE Conference on Evolutionary Computation, pp. 786–791. IEEE Press, Piscataway (1996)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D.E., Wang, L.: Adaptive Niching via Coevolutionary Sharing. IlliGAL Report No. 97007 (1997)Google Scholar
  8. 8.
    Yin, X., Germay, N.: A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization. In: Albrecht, R.F. (ed.) Proceedings of International Conference on Artificial Neural Nets and Genetic Algorithms, pp. 450–457. Springer, New York (1993)Google Scholar
  9. 9.
    Lin, C.Y., Liu, J.Y., Yang, Y.J.: Hybrid Multimodal Optimization with Clustering Genetic Strategies. Engineering Optimization 30, 263–280 (1998)CrossRefGoogle Scholar
  10. 10.
    Torn, A.: A Search-clustering Approach to Global Optimization. In: Dixon, S. (ed.) Towards Global Optimization, vol. 2, pp. 49–70. North-Holland, Amsterdam (1978)Google Scholar
  11. 11.
    Hanagandi, V., Nikolaou, M.: A Hybrid Approach to Global Optimization Using A Clustering Algorithm in a Genetic Search Framework. Computers Chem. Engng. 22(12), 1913–1925 (1998)CrossRefGoogle Scholar
  12. 12.
    Everitt, B.S.: Cluster Analysis, 3rd edn. John Wiley & Sons, New York (1993)Google Scholar
  13. 13.
    Deb, K., Goldberg, D.E.: An Investigation of Niche and Species Formation in Genetic Function Optimization. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms and their Applications, pp. 42–50. Morgan Kaufmann, San Mateo (1989)Google Scholar
  14. 14.
    Sareni, B., Krahenbuhl, L.: Fitness Sharing and Niching Methods Revisited. IEEE Transactions on Evolutionary Computation 2(3), 97–106 (1998)CrossRefGoogle Scholar
  15. 15.
    Harik, G.: Finding multimodal solutions using restricted tournament selection. In: Eshelman, L. (ed.) Proceedings of the 6th International Conference on Genetic Algorithms, pp. 24–31. Morgan Kaufmann, San Mateo (1995)Google Scholar
  16. 16.
    Goldberg, D.E., Deb, K., Horn, J.: Massive Multimodality, Deception, and Genetic Algorithms. In: Manner, R., Manderick, B. (eds.) Proceedings of the Second Conference on Parallel Problem Solving from Nature, pp. 15–25. North-Holland, Amsterdam (1992)Google Scholar
  17. 17.
    Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithm. In: Grefenstette, J.J. (ed.) Proceedings Of the Second International Conference on Genetic Algorithms and Their Applications, pp. 14–21. Lawrence Erlbaum, Hillsdale (1987)Google Scholar
  18. 18.
    Yu, X., Wang, Z.: The Fitness Sharing Genetic Algorithms with Adaptive Power Law Scaling. System Engineering Theory and Practice 22(2), 42–48 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xinjie Yu
    • 1
  1. 1.State Key Lab of Power Systems, Dept. of Electrical EngineeringTsinghua UniversityBeijingChina

Personalised recommendations